Bridge Processes: A Solution for LCI Datasets Independent of Background Databases

Area of Science:

• Environmental Science
• Sustainable Engineering

Background:

• Emerging platforms facilitate broader life cycle inventory (LCI) data publication.
• Incomplete LCI datasets pose integration challenges for LCA practitioners.
• Modifying original processes is often required to incorporate external data, compromising data integrity.

Purpose of the Study:

• To propose a novel solution for integrating fragmented LCI datasets into existing LCA models.
• To address the challenges of data sharing and usability in LCA.

Main Methods:

• Introduction of a 'bridge process' concept.
• Utilizing bridge processes to connect disparate LCI datasets and background databases.

Main Results:

• Bridge processes facilitate seamless integration of new datasets without altering original data.
• Enhanced model transparency and improved dataset sharing capabilities.
• Explicit data quality linkage and increased model flexibility.

Conclusions:

• Bridge processes offer a practical solution for integrating diverse LCI data within current LCA frameworks.
• While not a permanent fix, they enable better data interoperability and usability.
• Addresses limitations of fragmented data and proprietary process modifications.